The influence of risk perception on online shopping intention
Pham Van Tuan, Nguyen Dinh Trung/ MICA 2018 Proceedings
International Conference on Marketing in the Connected Age (MICA-2018), October 6th, 2018
Danang City, Vietnam
The Influence of Risk Perception on Online Shopping
Intention
Pham Van Tuana*, Nguyen Dinh Trungb
aFaculty of Marketing, National Economics University, Hanoi, Vietnam
bNational Economics University, Hanoi, Vietnam
A B S T R A C T
Online shopping as well as e-commerce have been widely expanding in a large number of countries over the
world, including Vietnam. In comparison with traditional methods, it brings many benefits but also contains
various limitations for consumers. Consumers have perceived numerous risks when shopping online and have
impacted on their buying intention. Thus, various domestic and overseas studies have been researched on
perceived risks and its influences on online shopping intention. Nevertheless, there were many differences
between researches’ conclusion about kinds of perceived risks and their impact ability to purchasing intention.
As such, this research is aimed at verifying all kinds of perceived risks introduced by previous studies in the
context of Vietnam, a case in Hanoi, to find out kinds of perceived risks that affect on online shopping
intention and evaluate its impact ability. By using quantitative method, the research used Cronbach’s Alpha,
EFA (Exploratory Factor Analysis) and multiple linear regression analysis to examine the model and
hypotheses. The results indicate that Information Security Risk has strongest impact on online shopping
intention; respectively following are Delivery Risk, Time Risk, Psycological Risk, Vendor’s Fraud Risk and
Product Performance Risk. Conclusions together with suggestions are offered to help organisations and
enterprises enhancing their online business efficiency and prestige
Keywords: Online shopping; online shopping intention; perceived risks; online shopping risks
1. Introduction
Online shopping on social networks and e-commerce has been growing rapidly in many countries around the
world, including Vietnam. According to a research conducted by Nielsen in 2016, there are 46% Vietnamese
customers said that they had bought a product or service through mobile devices in the first haft of the year, 92%
internet users in Ho Chi Minh city and Hanoi experienced online shopping activity. In addition, the growth rate
of the e-commerce market is quite good: revenue in 2015 reached $ 4.07 billion, increased about 37% compared
to the revenue in 2014 with the most popular goods and services purchased online are clothing, footwear and
cosmetics (Vietnam E-Commerce Report 2015 by the Ministry of Industry and Trade). However, the value that
online shopping contributes to our country is low when compared with other countries in the region and in the
world. This result might be because Vietnamese are still cautious about online shopping, they usually buy low
value products and services only. In addition, there are also a number of explanations that consumers have not
* Corresponding author. E-mail address: tuanmkt5888@gmail.com
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participated in online shopping yet, such as: the sellers are not reliable, the concept of buying at brick and mortar
is considered much more convenient and cheaper than online shopping, the fear feeling of disclosure of personal
information,...There are also the risks that customers feel before making a decision to take part in online
shopping activity.
The activity that promote online shopping, including e-commerce and business-related social networking,
plays an important role for businesses doing business in that field in particular and for the economy in general.
With purpose to achieve that activity, the first and foremost thing to do is building trust among customers,
reducing and eliminating the major risks that they experience before making an online shopping decision.
Therefore, it is necessary to identify the risk perceptors that affect the consumer’s online shopping intentions.
This study will delve into all the risk aspects that previous studies have used (including 9 aspects) emerging in
consumer perception when they pop up the intention of online shopping. For more detailed, the paper bases on
the knowledge in terms of Technology acceptance model (TAM) developed by Davis (1986) is a simple model
that has been used by academics for studying user accetance of new technologies, and recently for studying user
acceptance for online shopping; Theory of Planned Behavior (TPB) developed by Ajzen (1991) which has been
applied for predicting behaviors, Online Shopping Acceptance Model (OSAM) developed by Zhou et al. (2007)
primarily for consumers’adoption of online shopping and othe relating researches. From there, the study also
gives some suggestions from the approach of business administration for individuals and businesses which doing
online business.
2. Conceptual framework and research hypothesis
2.1. Risk perception
The concept of risk perception was first introduced by Bauer (1960) in the area of consumer behavior, which
is defined as the uncertainty of consumers in the purchase of the products/ service and must have the
responsibility for the consequence from this decision. He also noted that individuals can only respond and deal
with risk when they perceive it in a subjective way, and only the perception influences the decision of the
consumer. Perceived risk refer to the nature and amount of risk professed by a consumer in contemplating a
particular purchase decision (Cox & Rick, 1964). One of the most common definitions of perceived risk is the
perception of ambiguity and subjective expectation of loss (Sweeney, 1999). Some researchers also agree that
perceived risk is a double factors based on consumer perceptions of the degree of the success or failure (or
uncertainty) associated with uncertainty (or possible consequences) (Cox & Rich, 1964; Kim & Lennon, 2000).
Schiffman $ Kanuk (2000), who has the same view, argue that risk perception is the uncertainty that consumers
have to deal with when they can not forecast consequences of their purchase decisions.
Thus, many studies have given definitions of risk perception but in general the view on risk perception of
consumers agree that: Risk perception is a perception of consumers related to the uncertainty about unexpected
consequences in the buying decision process.
2.2. Online shopping intention
Intention refers to the extent of conscious effort that an individual will follow to approve his/her behaviour;
intention is also regarded as one of the motivational components of behaviour (Ajzen, 1991). Purchase intention
will occur when an individual plan to buy a particular commodity or service in the future. In the context of E-
Commerce, online purchase intention can be defined as a situation when a person desires to buy a particular
product or service through the website (Chen, Hsu & Lin, 2010; Fygenson & Pavlou, 2006).
Since the advancement of information technology, there have been models established by academics to
determine factors that mediate the susceptibility of users or consumers towards new information systems. One of
the most widely studied and applied models is the Technology Acceptance Model developed by Davis in the
1980s. His work showed that perceived usefulness (PU) and perceived ease of use (PEOU) are the factors that
determine the attitude towards new information systems that in turn affect the behavioural intention. This
intention would lead to actual use of the system, which is considered as a successful launch of the new
information system (Davis, 1985). Since its establishment, the TAM has been widely applied as a basis for
predicting user acceptance of new technology in various sectors, for in- stances, mobile phone adoption (Kwon
& Chidambaram, 2000), internet usage (Moon & Kim, 2001), e-learning (Park, 2009; Al-Adwan et al., 2013;
Sharma & Chandel, 2013), mobile-banking (Lule et al., 2012), etc. Lim and Ting (2012) applied TAM to
investigate the adoption behaviour of online shopping customers and found that both PEOU and PU would have
positive effect on attitude towards online shopping.
Fishbein and Ajzen developed the Theory of Reasoned Action (TRA) to ex- plain how different kinds of
behaviour were predicted by the concerned behavioural intention, which was further determined by the attitude
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towards the behaviour and subjec- tive norm. Ajzen later extended TRA to the theory of Planned Behaviour
(TPB) by adding the perceived behavioural control. Attitude towards a certain behaviour was said to be related
to the behavioural beliefs, which linked the behaviour to the perceived outcome and certain attributes like cost
incurred. Subjective norm was said to be related to the normative beliefs that were concerned with the likelihood
that important individuals or group members would approve or disapprove this certain behaviour. The perceived
behavioural control was said to be related to the control beliefs that were concerned with how easy or difficult
the behaviour was and how the behaviour was facilitated.
Zhou et al. (2007) developed a reference model for explaining customer acceptance of online shopping
through a survey of related studies. Their in-depth literature review revealed that an extensive number of factors
have been proven by empirical studies to influence customers’ online shopping intention and in turn the actual
online shopping behaviour.
Online shopping behaviour has been said to be directly associated with the behavioural intention in TAM,
TPB and OSAM. However, continuous online shopping intention is to be distinguished from first time shopping
intention. First time shopping can be either shopping at a new website or shopping at online websites instead of
traditional bricks-and-mortar stores. Zhou et al. also identified that past online shopping satisfaction had a
greater effect on continuous buying intention, while innovativeness and perceived usefulness had a greater
moderating effect on first time online intention. In OSAM, online experience, consumers’ innovativeness,
attitude towards online shopping, normative beliefs and satisfaction are positively associated with online
shopping intention, while shopping motivation and shopping orientation have an influence on online shopping
intention.
2.3. The aspects of risk perception affect the intention of online shopping
Risk perception is a multidimensional phenomenon (Cunningham, 1967) and each of those dimension is cost
estimate at different view in the future, contributing to the perceived value of the product (Sweeney & Soutar,
1999; Sweeney, Soutar, & Johnoson, 2001). Though an overview of national and international research, the
author found out that there has been not any study has assessed the overall dimension of risk perception. Each
study selected several aspects that satisfy the resources, context, subjects and scope of the research. In addition,
there is a contradictory that a affirmative dimension that influences the intention of online shopping in one study
but is negated in other studies. The lastest dosmetic research by Ha Ngoc Thang & Nguyen Thanh Do (2016) on
the factors affecting the online shopping intentions of Vietnamese consumers just examined only two aspects of
risk perception are financial risk and product risk. In a more in-depth study of risk perception, Bui Thanh Trang
(2013) has pointed out four types of risk perceptions that affect on online shopping intetions: financial risk,
product risk, information security risk, fraud risk of the vendor. In terms of foreign studies, Bertea (2004)
suggested that there are three types of risk perceptions: financial risk, product risk and time risk, while Bo Dai,
Sandra Forsythe and Wi-Suk Kwon (2013), three types of risk perception affecting in online shopping intention
are product risk, financial risk and information security risk, and Hashim (2015) mentioned three concepts are
financial risk, product risk and delivery risk. However, according to Shannon-Jane Ward (2008), Mohammad
(2012) and Chiu Chen (2015), for online shopping perspective, people may experience six basic types of risk
perception which are product risk, financial risk, physical risk, social risk, psychological risk and time risk. The
research conducted by Emad Masoud (2013) evaluated the effect of six types of risk perceptions are: financial
risk, product risk, delivery risk, informational security risk, time risk, and social risk; and there are only the four
first types of those risk perceptions have negative effect on online shopping behavior, while the risk of time and
social risk do not have such kind of affect.
In conclusion, basing on the theory of previous studies, the author expects to develop the most
comprehensive study of all aspects of risk perception and identify how each type of risk perception affect on
online shopping intention. The proposed research model, through literature review, there are nine types of risk
perception which are Financial risk, Product risk, Time risk, Physical risk, Psychological risk, Social risk,
Delivery risk, Information security risk and Vendor’s Fraud risk and all types of risk perception are shown in
Figure 1.
Finacial risk perception is a perception of the potetial for fianancial loss from online shopping behavior
(Jacoby & Kaplan, 1972), and it is also a perception of possibility of losing money through online purchase
(Horton, 1984). In particular, the phenomenom of credit card fraud and financial loss are considered two of the
top concerns of online consumers (Sweney & Johnson, 1999). In addition, financial risk includes shipping
charges which customer have to pay mentioned by Turner (1999) and hidden charges pointed out by Krantz
(2003). On the other hand, the purchase of problematic or unsatisfactory products from unreliable suppliers is
also likely to increas the cost of online purchases (Lim, 2003). Hypothesis H1 is given:
H1: Financial risk perception has negative effect on online purchase intention.
Product risk perception is perception of possibility of customers’ expectations for product/ service are not
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fulfilled after having purchasing (Horton, 1976). Product risk occurs when the product/ service does not work as
expected and the buyer can not evaluate the real quality of the product (Bhatnagar, Misra & Rao, 2000). When
having online shopping activity, buyers rely on only limited information and images displayed on website so that
they can not feel product directly (Jarvenpaa, Noam & Lauri, 1999) which leads to the uncertain feeling when
consumers have intention of online shopping. Hypothesis H2 is given:
H2: Product perception risk has negative effect on online purchase intention.
Time risk perception is perception about time that the buyer use for buying product (Griffin & Viehland,
2010). According to Sim & Su (2012), time perception risk involves the waste of time, access ability or effort to
make any purchasing decision and when purchased product needs to be repaired. In fact, while shopping online,
cosumers might waste their time in researching product information. Other time risks also include the
inconvienience that buyers deal with dủing online transaction, navigation and order procedure, as well as delays
in purchasing products (Forsythe, Liu, Shannon & Gardner, 2006). Hypothesis H3 is given:
H3: Time risk perception has negative effect on online purchase intention.
Physical risk perception is perception about the possibility and importance that consumers associate with a
product that has negative effect on their health (Lim, 2003). When having online shopping, consumers lack
interaction with other leading to physical risks. According to Swinyard & Smith (2003), there is not any sales
assistant who can tailor product information based on the specific needs of consumers; therefore, there might be
a risk or harm when consumers misuse product. Hypothesis 4 is given:
H4: Physical risk perception has negative effect on online purchase intention.
Psychological risk perception is a perception about the possibility and importance that the individual is
stressed by his buying behavior (Lim, 2003). Internet shopping as well as traditional shopping will cause a sense
of disapointment and shame to the buyers when the product that they have bought did not match with their own
image (Forsythe & Shi, 2003). When having online shopping, consumers might have uncomfortable feeling due
to disclosure of personal information, which can be discribed as a psychologiacal risk (Forsythe & Shi, 2003).
Hypothesis H5 is given:
H5: Psychological risk perception has negative effect on online shopping intention.
Social risk perception is a perception about possibility and importance of the perception of others be affected
by an individual’s online shopping behavior (Lim, 2003). Social risk includes the possibility of online shopping
products can influence the way that others think about buyers as well as their buying behavior will not be
accepted by other members of society. The way that not deliver holiday presents in time might cause negative
effect on social interactions with friends and lead to social risk that online shoppers can feeel (Mardesich, 1999).
Hypothesis H6 is given:
H6: Social risk perception has negative effect on onling shopping intention.
Delivery risk perception is a perception of the loss during delivery which relate to lost or damaged goods,
or shipping wrong product after customer finished an online transaction procedure (Zhang, Tan, Xu, & Tan,
2012). Customers may have worry feeling about late delivery or they even not receive the product. Hypothesis
H7 is given:
H7: Delivery risk perception has negative effect on online shopping intention.
Information security risk perception is a perception of possibility of personal information being lost,
disclosed, or unsecured during online transaction (Garbarino & Strahilevitz, 2004). The risk of disclosure of
personal information while shopping online is being concerned increasingly (Drenman, 2006). There are a
number of people use unauthorized information without the allowance of the information owner, which raises
concerns about the buyers information stolen while they provide personal information such as bank account
number, address, phone number, email when buying goods online. Hypothesis H8 is given:
H8: Information security risk perception has negative effect on online shopping intention.
Vendor’s Fraud risk perception is a perception of buyers about the dishonest of the seller, such as
inaccurate information about product quality, promotion policy, customer preferences; failure to comply with
post-sale commitments; the ability of buyers to find a pace to deal with a dispute (Mc CorKle, 1990). Hypothesis
H9 is given:
H9: Vendor’s Fraud risk perception has negative effect on online shopping intention.
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Fig. 1. Research Model
Resource: Proposed by authors
3. Research methodology
The study was conducted in two steps: preliminary investigation and formal investigation. In the preliminary
investigation, the authors used qualitative research, conducted in-depth interviews, and in-depth interviews with
a number of experts and clients who had previously shopped online to adjust the scale and complete the
questionnaire to suit the situation in Hanoi, Vietnam. Note that the questionnaire was based on a literature
review, and the scales were checked by previous studies, so the authors only inherited and adapted the words for
suitable purpose. The questionnaire consists of three parts. The first part measures consumers' perceptions of risk
when shopping online. According to the study of Bui Thanh Trang (2013), there are the financial, product and
fraud of the seller risk; time risk and physical risk of Hassan et al. (2006); social risk and psychological risk are
correlated from the scale of Corbitt & Thanasankit (2003) and Hassan et al. (2006); delivery risk by Hashim
Shahzad (2015); information security risk is the combination of Bui Thanh Trang (2013) and Chiu Chen (2015).
The second part measures online shopping intentions, using the scale of Bui Thanh Trang (2013). Components
of the model, including risk perceptions and online shopping intentions, were measured using a Likert scale of 5,
with 1 being very disagreeable and 5 very agreeable. The third section is information on demographics and
online shopping behavior, including questions about gender, age, education, income and years of online
shopping, using the nominal scale.
Then, the completed questionnaire was sent to the official quantitative survey in Hanoi from April 15, 2017
to May 1, 2017. Research subjects are Internet users, who have experienced online shopping, they belong to all
range of ages, places of living, income, different levels. Sample subjects are customers over 18 who have
experienced online shopping. The measurement model consists 34 observations, based on the principle of a
minimum of 5 elements per measurement variable (Bentler & Chou, 1987), the minimum sample size required is
34*5= 170, then, the chosen sample size is 200. The questionares are sent directly to respondents and via Internet
with random sampling method combines convenient sampling. The result collected 177 valid responses, with
response rate of 88.5%. The characteristics of the sample in Table 1 show that there are 78% females, 65.5%
consumers are from 18 to 36 years old and 66.7% of consumers have two-year experience in online shopping.
Data processing is performed through the following steps: (i) verifying the scale and reliability of measurement
variables by Cronbach’s Alpha coefficient and value by Exploratory Factor Analysis (EFA); (ii) linear multiple
regression analysis for model testing and research hypothesis.
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Table 1. Characteristics of samples
Freque
ncy
Sample size (n=177)
Percent (%)
Male
39
22,0
Sex
Female
138
49
78,0
27,7
14,7
23,2
From 18 to 25
From 26 to 30
From 31 to 35
26
Age
41
Over 36
61
34,5
Under 3 million
36
81
45
15
11
20,3
45,8
25,4
8,5
Average
income per
month
From 3 to 6 million
From 6 to 10 million
Over 10 million
Graduated High school
6,2
Graduated college/
professional high school
20
83
59
4
11,3
46,9
33,3
2,3
Education
level
Graduated university
Graduated post-
university
Other
Number of
years
participating From 1 to 2 years
Under 1 year
67
51
59
37,9
28,8
33,3
online
Over 2 years
shopping
Resource: Proposed by authors
4. Results
4.1. Verification of the scale
Using SPSS 20.0 software, the author drew the results of reliability analysis of the scale (Cronbach Alpha)
and the results of the Exploratory Factor Analysis (EFA), which suggested that the removal and consolidation of
a number of observations to help the scale more accurately assess to the concepts.
The first step is conducting a Cronbach’s Alpha test to examine the reliability of the measurement. This step
is used to delete unreliable variables, avoid the case of that variables make unrealistic factor when analyzing
Exploring Factor Analysis EFA (Churchill, 1979). The Cronbach’s Alpha coefficient must be greater than 0.6
and the correlation coefficient of each scale must be greater than 0.3 (Hair et al., 2006). The result in Table 2
shows that all scales of risk perceptions and online shopping intention satisfy the criteria. Thus, all scales of
factors are reliable and continued to be used to analyze upcoming factor.
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Table 2. Summary of Reliability Statistics and minimum Corrected Item-Total Correlation
Number of
Minimum
Cronbach’s
Items
Scale
correlation
coefficient
Alpha
Product risk (SP)
Finacial risk (TC)
Time risk (TG)
4
3
3
3
0,832
0,700
0,697
0,819
0,605
0,473
0,438
0,659
Physical risk (THC)
Social risk (XH)
3
3
3
4
4
4
0,887
0,794
0,738
0,891
0,874
0,831
0,664
0,579
0,472
0,721
0,641
0,569
Psychological risk (TL)
Delivery risk (GH)
Information srcurity risk (BMTT)
Fraud of seller risk (GLNB)
Online shopping intention (YD)
Resource: Research of authors
After testing Cronbach's Alpha, the next step is to analyze the EFA exploration factor for a preliminary
evaluation of the uniqueness, convergence value, and discriminant value of the scale. The EFA exploratory
factor analysis was performed using the Principal Component Analysis method and the Varimax rotation to
group the factors. With a sample size of 177, the factor loading of items must be greater than 0.5; The variables
converge on the same factor and distinguish it from others (Hair & ctg, 2009). In addition, the KMO test
coefficient must be in the range of 0.5 to 1. After analyzing the first factor, the TC3 variable is deleted because it
does not satisfy the required conditions, take a second analysis with 33 items.
The analysis results in Table 3 show that all loading factor of the items are greater than 0.5; Bartlett test with
significance level Sig. = 0.000 with coefficient KMO = 0.861. All 33 variables after EFA analysis were extracted
into eight factors with values of Eigenvalues greater than 1 and Variance Explained greater than 50%. The
remaining eight factors from the nine factors are due to the observed variables of Financial Risk and Physical
Risk that converge on one factor, and this factor is named Physical Risk (VC). The research model changes into
8 independent variables, with the new variable Material Risk, and one dependent variable, used for the linear
regression analysis and subsequent hypothesis testing.
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Table 3. EFA analysis about online shopping risk perception
Factor
Items
1
2
3
4
5
6
7
8
9
Online
YD3
0,878
shoppin
g
intentio
n
YD1
YD4
YD2
0,872
0,850
0,839
(YD)
Informat BMTT1
0,808
0,807
0,778
0,750
ion
BMTT3
BMTT2
BMTT4
security
risk
(BMTT)
SP1
SP3
0,832
0,805
Product
risk(SP)
SP4
SP2
VC1
0,747
0,715
0,779
VC2
VC3
0,749
0,720
Physical
risk(VC
)
VC4
VC5
0,535
0,509
GLNB4
0,790
Vendor’
s Fraud
Risk
GLNB3
GLNB2
0,783
0,780
(GLNB)
GLN
B1
0,6
13
XH3
XH2
0,905
0,895
Social
risk
(XH)
XH1
TL1
0,736
0,793
Psychol
ogical
(TL)
TL2
TL3
GH2
0,763
0,683
0,720
Delivery
risk(GH
)
GH3
GH1
TG3
TG1
TG2
0,713
0,674
0,785
0,714
0,580
Time
risk
(TG)
Resource: Research of authors
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4.2. Regression analysis and hypothesis testing
First, use Pearson correlation analysis to analyze the correlation between quantitative variables. At a
significance level of 5%, the correlation coefficients show that the relationship between the dependent variable
and the independent variables is statistically significant (Sig value is less than 0.05). The magnitude of the
correlation coefficients ensures that there are no hyperbolic phenomena. Therefore, it is possible to use other
statistics to test the relationship between variables.
Next, conduct a linear multiple regression analysis of the relationship between risk factors that influence
online shopping intentions. As shown in Table 4, the adjusted R2 coefficient is 0.541, indicating that the linear
regression model was constructed in accordance with the data set of 54.1%. The results of the regression indicate
that the six independent variables are: Product Risk, Psychological Risk, Time Risk, Information Security Risk,
Vendor’s Fraud Risk and Delivery Risk have The beta-denominated Beta is -0.138; -0.165; -0,172; -0.338; -
0,165 and -0,201 with significance levels less than 0.05. Thus, the hypotheses H2, H3, H5, H7, H8, H9 are
accepted. Social Risks, Physical Risks and Social Risks has Sig. are 0.245 and 0.080 which are greater than 0.05,
so the hypotheses H1, H4 and H6 are rejected.
Based on the Beta, it can be concluded: Information security Risk have the greatest impact on online
shopping intentions; then to Delivery Risk, Time Risk, Psychological Risk, and Fraud from seller Risk. Product
risk is the worst influencing factor for online shopping. The linear regression model obtained is as follows:
YD = 8,061 – 0,381 * BMTT – 0,201 * GH – 0,172 * TG – 0,165 * TL – 0,165 * GLNB – 0,138 * SP + e
Table 4. The regression results of risk perception factors affect on online shopping intention.
Model
Unstandardized
Coefficients
Standardiz-ed
Coefficients
t
Sig.
Collinearity
Statistics
B
Std. Error
0,313
Beta
Tolerance
VIF
(Constant)
8,061
-0,138
-0,087
25,716 0,000
-2,029 0,044
-1,165 0,245
-2,210 0,028
-2,068 0,040
Product Risk(SP)
Social Risk (XH)
0,068
0,075
-0,112
-0,063
-0,122
-0,115
0,663
0,706
0,667
0,661
1,508
1,416
1,499
1,512
Psychological Risk(TL) -0,165
0,075
0,083
Time Risk (TG)
-0,172
-0,381
-0,171
-0,165
-0,201
Information security
Risk (BMTT)
Physical Risk (VC)
Vendor’s Fraud Risk
(GLNB)
0,072
0,097
0,076
0,084
-0,306
-0,113
-0,128
-0,144
-5,274 0,000
-1,760 0,080
-2,165 0,031
-2,406 0,017
0,603
0,495
0,585
0,567
1,658
2,020
1,709
1,763
Delivery Risk (GH)
Dependent Variable: Online shopping intention (YD)
Adjusted R square = 0,541
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Resource: Research of authors (2017)
5. Conclusion and recommendation
Research results show that risk perception factors have affect on online shopping intention including:
Information security Risk, Delivery Risk, Time Risk, Psychological Risk, Vendor’s Fraud Risk và Product Risk.
All of these factors have negative effect on shopping intention. In particular, Information security Risk factor
hase the biggest effect on online shopping intention, suggesting that buyers feel worry and unsecured when
thinking about their personal information is unsafe when shopping onling and they also have to bear an annoying
from strangers. This is can be explained by the importance of information the current situation of information
insecurity. Financial Risk, Physical Risk, Social Risk factors do not show the effect on onling shopping
intention, which show the contradictory to the results of some previous studies, especially the research of Bui
Thanh Trang (2013) when the result confirm that Financial Risk has the most negative effect on online shopping
intention. It can be seen that online sellers have put a lot of efforts to limit the risk of spending money when
buyers have online purchase such as cash on delivery, checking product before receiving, having high possibility
of refund; therefore, all of these activities make buyers more secure and less worried about the financial risk
involving online shopping.
In general, individuals and organizations with online businesses are now trying to improve and round the
quality of their products and services, minimizing the risks that customers may have, making customers get ease
when buying. However, there are risk aspects that they have not dealt with well or have not done yet, leading to
the hesitation of customers when buying goods online. For that reason, the authors propose some of the
managament proposals for individuals and businesses of online businesses as follows:
First, commit to the customer that their personal information will not be public and fully encrypted to ensure
confidentiality. Do not disclose customer information to third parties without their permission.
Second, if the company has the ability to deliver goods by themselve, training and having strict monitoring
policy for delivery staff. If the business is not capable of self-delivery and must outsource, it is important to
choose reputable delivery companies to minimize the risk of delivery and help customers peace of mind
shopping.
Third, ensure delivery of products to customers on time; communicate the benefits of online shopping and
shows that online shopping is suitable for anyone actively, regardless of wealth or class; create trust of customers
with the seller itself by letting customers check the product after delivery, provide their information online,
immediately deal with the problem wrong ... This measure minimizes customer perceptions of time, psychology,
and vendor’s fraud when shopping online.
Fourth, ensure the quality of products delivered to customers according to the commitments on the network.
Publishing fully, clearly and accurately information on the products, especially the image of the products'
representatives should be truthful, clear, on all media so that the customers can freely refer and comment about
products. When customers have a comprehensive and correct view of the product, they will feel more confident
in their purchase and avoid negative feedback from the information and experience after receiving the product.
6. Limitations and implications
Limitations of this study: Firstly, the study use a convenient sampling method, so the sample does not really
guarantee the representation; Secondly, it is not possible to use higher-order theoretical modeling methods;
Thirdly, it is not yet possible to research subjects who have used the internet but have never shopped online, and
the target groups that organizations and businesses sell online are also particularly interested in attracting them to
become customers, and compare this group with the group which have bought their products. Based on these
limitations, the authors suggest further studies should use the SEM model and analyze the ANOVA variance
between two groups that have never been and have been online shopping. The SEM model allows simultaneous
estimation of elements in the model, the causal relationship between concepts, the measurement of stable and
unstable relationships, direct influence factors as well as indirect one. ANOVA analysis to comparing the mean
of multiple samples based on average samples and through hypothesis testing to conclude whether there is a
difference in the degree of influence of the independent variable on the dependent variable between the two
groups Never and never bought.
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